We develop a recursive neural network (RNN) to extract answers to arbitrary natural language questions from supporting sentences, by train- ing on a crowdsourced data set (to be released upon presentation). The RNN defines feature representations at every node of the parse trees of questions and supporting sentences, when applied recursively, starting with token vectors from a neural probabilistic language model. In contrast to prior work, we fix neither the types of the questions nor the forms of the answers; the system classifies tokens to match a sub- string chosen by the question?s author. Our classifier decides to follow each parse tree node of a support sentence or not, by classify- ing its RNN embedding together with those of its siblings and the root node of the question, until rea...